Adaptive supervisory Gaussian-cerebellar model articulation controller for direct torque control induction motor drive

This study develops an adaptive supervisory Gaussian-cerebellar model articulation controller (ASGCMAC) and implements it in a sensor-less direct torque control (DTC) system for controlling induction motor speed. The inherent uncertainties of a DTC system, including parametric uncertainties and the uncertainties in external load torque, make control tasks difficult. A model-free approach, ASGCMAC, is adopted to build a high-performance DTC induction motor drive. The proposed method comprises two parts - a supervisory controller and a Gaussian-CMAC (GCMAC) subsystem. The supervisory controller monitors the control process to maintain the tracking error within a pre-defined range; the GCMAC sub-system learns and approximates system dynamics. The parameters of ASGCMAC are adjusted online according to adaptive rules, which are derived from Lyapunov stability theory, guarantee system stability. Four control schemes, ASGCMAC, supervisory controller, proportional-integral (PI) control and conventional CMAC, are experimentally investigated and the performance index, root-mean-square error (RMSE), is evaluated in each scheme. The results reveal that ASGCMAC outperforms the other comparison schemes. In addition, the robustness of the proposed scheme to the parameter variation and external load torque disturbance has been verified via simulation and experiments.

[1]  Frede Blaabjerg,et al.  Variable-structure direct torque control - a class of fast and robust controllers for induction machine drives , 2004, IEEE Transactions on Industrial Electronics.

[2]  Youyi Wang,et al.  Control Synthesis of Continuous-Time T-S Fuzzy Systems With Local Nonlinear Models , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Faa-Jeng Lin,et al.  Recurrent wavelet-based Elman neural network control for multi-axis motion control stage using linear ultrasonic motors , 2010 .

[4]  P. A. Witting,et al.  Direct Torque Control of Induction Motors Utilizing Three-Level Voltage Source Inverters , 2008, IEEE Transactions on Industrial Electronics.

[5]  I. Takahashi,et al.  High-performance direct torque control of an induction motor , 1989 .

[6]  Fayez F. M. El-Sousy.  Robust adaptive H ∞ position control via a wavelet-neural-network for a DSP-based permanent-magnet synchronous motor servo drive system , 2010 .

[7]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[8]  Rong-Jong Wai,et al.  Robust Petri Fuzzy-Neural-Network Control for Linear Induction Motor Drive , 2007, IEEE Transactions on Industrial Electronics.

[9]  Y. S. Lai.  Machine Modeling and Universal Controller for Vector-Controlled Induction Motor Drives , 2002, IEEE Power Engineering Review.

[10]  Toshihiko Noguchi,et al.  A New Quick-Response and High-Efficiency Control Strategy of an Induction Motor , 1986, IEEE Transactions on Industry Applications.

[11]  Rong-Jong Wai,et al.  Adaptive Fuzzy Neural Network Control Design via a T–S Fuzzy Model for a Robot Manipulator Including Actuator Dynamics , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Faa-Jeng Lin,et al.  An intelligent control for linear ultrasonic motor using interval type-2 fuzzy neural network , 2008 .

[13]  O. A. Mahgoub,et al.  Implementation of a new fast direct torque control algorithm for induction motor drives , 2010 .

[14]  Faa-Jeng Lin,et al.  Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism , 2006, IEEE Transactions on Neural Networks.

[15]  Yen-Shin Lai,et al.  New hybrid fuzzy controller for direct torque control induction motor drives , 2003 .

[16]  Faa-Jeng Lin,et al.  Recurrent wavelet neural network controller with improved particle swarm optimisation for induction generator system , 2009 .

[17]  Hiok Chai Quek,et al.  Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence , 2007, IEEE Transactions on Neural Networks.

[18]  Mikihiko Matsui,et al.  Fuzzy-Logic-Based $V/f$ Control of an Induction Motor for a DC Grid Power-Leveling System Using Flywheel Energy Storage Equipment , 2009, IEEE Transactions on Industrial Electronics.

[19]  Peter Vas,et al.  Sensorless vector and direct torque control , 1998 .

[20]  Jafar Soltani,et al.  Adaptive Nonlinear Direct Torque Control of Sensorless IM Drives With Efficiency Optimization , 2010, IEEE Transactions on Industrial Electronics.

[21]  Chih-Min Lin,et al.  Adaptive CMAC-based supervisory control for uncertain nonlinear systems , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  I. Choy,et al.  Nonlinear uncertainty observer for AC motor control using the radial basis function networks , 2004 .

[23]  James S. Albus,et al.  Data Storage in the Cerebellar Model Articulation Controller (CMAC) , 1975 .

[24]  F. Blaabjerg,et al.  Direct torque control of sensorless induction motor drives: a sliding-mode approach , 2004, IEEE Transactions on Industry Applications.

[25]  Wen Ding,et al.  Modelling and predicting of a switched reluctance motor drive using radial basis function network-based adaptive fuzzy system , 2009 .

[26]  Ming-Feng Yeh,et al.  A self-organizing CMAC network with gray credit assignment , 2006, IEEE Trans. Syst. Man Cybern. Part B.

[27]  Chun-Shin Lin,et al.  Learning convergence of CMAC technique , 1997, IEEE Trans. Neural Networks.